def test(self, x_test, y_test, params, n_centers, width):
y_true = []
y_pred = []
(p, _) = x_test.shape
for i in range(p):
d = y_test[i]
y = self.predict(x_test[i], params, n_centers, width)
# Confusion Matrix
y_true.append(list(d))
y_pred.append(list(y))
a = util.inverse_transform(y_true, self.n_classes)
b = util.inverse_transform(y_pred, self.n_classes)
return acc(a, b), tpr(a, b,
average='macro'), 0, ppv(a,
b,
average='weighted')
def test(self, x_test, y_test):
(m, _) = x_test.shape
y_actu = []
y_pred = []
for i in range(m):
xi = x_test[i]
y, _ = self.predict(xi)
d = y_test[i]
# Confusion Matrix
y_actu.append(list(d))
y_pred.append(list(y))
a = util.inverse_transform(y_actu)
b = util.inverse_transform(y_pred)
cm = ConfusionMatrix(a, b)
#cm.print_stats()
#util.plotConfusionMatrix(cm)
return cm.ACC, cm.TPR, cm.SPC, cm.PPV
